Table REF  illustrates a layer-by-layer definition of our neural network model. First, there are two stacked LSTM layers with rectified activation functions (ReLUs) and input-output dimensions of \((K, P)\) -\((K, 20)\) and \((K, 20)\) -\((10)\) , respectively. \(K := 2\)  is the number of consecutive minutes per input feature, and \(P := 12\)  is the number of parameter values per minute. Finally, there is a fully connected layer (i.e. Dense) with a sigmoid activation function with input-output dimensions of \((10)\) -\((1)\) . Both modeling and training processes are performed with Tensorflow and Keras software libraries [1]}[2]}.
